In MR imaging parallel imaging techniques and iterative reconstruction methods have produced very promising methods, which allow the recording time for producing an MR image to be reduced or a higher spatial resolution to be achieved for the same recording time. The main objective is generally to reconstruct the “best” possible MR images from undersampled MR data, in other words to reconstruct the MR images without filling the associated raw data space or k space completely with MR data. The problems that occur with such methods are generally what are known as aliasing artifacts or slow convergence rates during iterative reconstruction, generally resulting in long reconstruction times.
With iterative reconstruction methods the missing MR data that has not been recorded is supplemented by prior knowledge of the expected image. This prior knowledge feeds into the optimization by means of so-called regularization terms or penalty terms performed during the reconstruction method. With such iteration methods only generic assumptions are made about the MR images to be reconstructed. Information tailored to the specific examination object is not used. The generic assumptions generally made about the image to be reconstructed can for example include general information about edges in medical images.